In addition to the mentioned refinement steps, we implement an iterative training strategy in which the domain adaptive network is trained and re-trained a series of times with the source model \(\phi _s\)  and pseudo labels re-initialized to the previously trained model and generated predictions respectively. For the implementation of SECOND-iou, we use the public pyTorch repository Open-PCDet [1]}, and the official code release from [2]} for the implementation of PointRCNN. We perform experiments with a 48GB Quadro RTX 8000 GPU and a 16GB GeForce RTX 2080 GPU. We follow the lengths of training recommended by the authors in the case of each object detector network. In both cases, the cyclic Adam optimization algorithm is used.
